PaperPlayer biorxiv bioinformatics

Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.30.362533v1?rss=1

Authors: Kockmann, T., Panse, C.

Abstract:

The Bioconductor project has shown that the R statistical environment is a highly valuable tool for genomics data analysis [DOI:10.1038/nmeth.3252], but with respect to proteomics we are still missing low level infrastructure to enable performant and robust analysis workflows in R. Fundamentally important are libraries that provide raw data access. Our R package rawDiag [DOI:10.1021/acs.jproteome.8b00173] has provided the proof-of-principle how access to mass spectrometry raw files can be realized by wrapping vendor-provided APIs, but rather focused on meta data analysis and visualization. Our novel package rawR now provides complete, OS independent access to all spectral data logged in Thermo Fisher Scientific raw [URL:https://github.com/fgcz/rawR] files. In this technical note, we present implementation details and describe the main functionality provided by the rawR package. In addition, we report two use cases inspired by real-word research task that demonstrate the application of the package.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.11.02.365346v1?rss=1

Authors: Wang, L., Zhang, J., Cai, J.

Abstract:

Recently we developed scCapsNet, an interpretable deep learning cell type classifier for single cell RNA sequencing data, based on capsule network. However, the running process of scCapsNet is not fully automatic, in which a manual intervention is required for getting the final results. Here we present scCapsNet-mask, an updated version of scCapsNet that utilizes a mask to fully automate the running process of scCapsNet. scCapsNet-mask could constrain the internal parameter coupling coefficients and result in a one to one correspondence between the primary capsule and type capsule. Based on those bijective mapping between primary capsule and type capsule, the model could automatically extract the cell type related genes according to weight matrix connecting input and primary capsule, without a need for manual inspection of the relationship between primary capsules and type capsules. The scCapsNet-mask is evaluated on two single cell RNA sequence datasets. The results show that scCapsNet-mask not only retains the merits of the original scCapsNet with high classification accuracy and high interpretability, but also has the virtue of automatic processing

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.11.02.364612v1?rss=1

Authors: Lanza, B., Parashar, D.

Abstract:

Biomarkers are known to be the key driver behind targeted cancer therapies by either stratifying the patients into risk categories or identifying patient subgroups most likely to benefit. However, the ability of a biomarker to stratify patients relies heavily on the type of clinical endpoint data being collected. Of particular interest is the scenario when the biomarker involved is a continuous one where the challenge is often to identify cut-offs or thresholds that would stratify the population according to the level of clinical outcome or treatment benefit. On the other hand, there are well-established Machine Learning (ML) methods such as the Support Vector Machines (SVM) that classify data, both linear as well as non-linear, into subgroups in an optimal way. SVMs have proven to be immensely useful in data-centric engineering and recently researchers have also sought its applications in healthcare. Despite their wide applicability, SVMs are not yet in the mainstream of toolkits to be utilised in observational clinical studies or in clinical trials. This research investigates the very role of SVMs in stratifying the patient population based on a continuous biomarker across a variety of datasets. Based on the mathematical framework underlying SVMs, we formulate and fit algorithms in the context of biomarker stratified cancer datasets to evaluate their merits. The analysis reveals their superior performance for certain data-types when compared to other ML methods suggesting that SVMs may have the potential to provide a robust yet simplistic solution to stratify real cancer patients based on continuous biomarkers, and hence accelerate the identification of subgroups for improved clinical outcomes or guide targeted cancer therapies.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.11.01.363887v1?rss=1

Authors: Jain, C., Rhie, A., Hansen, N., Koren, S., Phillippy, A. M.

Abstract:

About 5-10% of the human genome remains inaccessible for functional analysis due to the presence of repetitive sequences such as segmental duplications and tandem repeat arrays. To enable high-quality resequencing of personal genomes, it is crucial to support end-to-end genome variant discovery using repeat-aware read mapping methods. In this study, we highlight the fact that existing long read mappers often yield incorrect alignments and variant calls within long, near-identical repeats, as they remain vulnerable to allelic bias. In the presence of a non-reference allele within a repeat, a read sampled from that region could be mapped to an incorrect repeat copy because the standard pairwise sequence alignment scoring system penalises true variants. To address the above problem, we propose a novel, long read mapping method that addresses allelic bias by making use of minimal confidently alignable substrings (MCASs). MCASs are formulated as minimal length substrings of a read that have unique alignments to a reference locus with sufficient mapping confidence (i.e., a mapping quality score above a user-specified threshold). This approach treats each read mapping as a collection of confident sub-alignments, which is more tolerant of structural variation and more sensitive to paralog-specific variants (PSVs) within repeats. We mathematically define MCASs and discuss an exact algorithm as well as a practical heuristic to compute them. The proposed method, referred to as Winnowmap2, is evaluated using simulated as well as real long read benchmarks using the recently completed gapless assemblies of human chromosomes X and 8 as a reference. We show that Winnowmap2 successfully addresses the issue of allelic bias, enabling more accurate downstream variant calls in repetitive sequences. As an example, using simulated PacBio HiFi reads and structural variants in chromosome 8, Winnowmap2 alignments achieved the lowest false-negative and false-positive rates (1.89%, 1.89%) for calling structural variants within near-identical repeats compared to minimap2 (39.62%, 5.88%) and NGMLR (56.60%, 36.11%) respectively. Winnowmap2 code is accessible at https://github.com/marbl/Winnowmap

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.11.01.364240v1?rss=1

Authors: Pires, D. E. V., Stubbs, K. A., Mylne, J. S., Ascher, D. B.

Abstract:

Herbicides have revolutionised weed management, increased crop yields and improved profitability allowing for an increase in worldwide food security. Their widespread use, however, has also led to not only a rise in resistance but also concerns about their environmental impact. To help identify new, potent, non-toxic and environmentally safe herbicides we have employed interpretable predictive models to develop the online tool cropCSM (http://biosig.unimelb.edu.au/crop_csm).

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.31.362988v1?rss=1

Authors: Christensen, E. R., Naidas, A., Husic, M., Shooshtari, P.

Abstract:

Tumour microenvironments (TME) contain a variety of cells including but not limited to stromal fibroblasts, endothelial cells, immune cells, malignant cells, and cells of the tissues of origin, whose interactions likely influence tumour behaviour and response to cancer treatment. The specific composition of the TME can be elucidated using single-cell RNA sequencing (scRNA-seq) by measuring the expression profiles of individual cells. Several scRNA-seq datasets from multiple cancer types have been published in recent years, yet we still lack a comprehensive database for the collection and presentation of TME data from these studies in an easily accessible format. We have thus built a database of TME scRNA-seq data, containing 21 TME scRNA-seq datasets from 12 different cancer types. We have also created an R package called TMExplorer, which provides an interface to easily search and access all available datasets and their metadata. Data and metadata are kept in a consistent format across all datasets, with multiple expression formats available depending on the use case. Users can view a table of metadata and select individual datasets or filter them by specific characteristics. Users may also select a specific type of cancer and view all published scRNA-seq data for that cancer type available in our database. Users are provided with an option to save the data in multiple formats in order to view or process it outside of R. Thus, the TMExplorer database and search tool allow for thorough examination of the TME using scRNA-seq in a way that is streamlined and allows for easy integration into already existing scRNA-seq analysis pipelines.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.11.01.363960v1?rss=1

Authors: Gregory, A. C., Gerhardt, K., Zhong, Z.-P., Bolduc, B., Temperton, B., Konstantinidis, K. T. C., Sullivan, M. B.

Abstract:

Background: Microbes and their viruses are hidden engines driving Earth's ecosystems from the oceans and soils to humans and bioreactors. Though gene marker approaches can now be complemented by genome-resolved studies of inter- (macrodiversity) and intra- (microdiversity) population variation, analytical tools to do so remain scattered or under-developed. Results: Here we introduce MetaPop, an open-source bioinformatic pipeline that provides a single interface to analyze and visualize microbial and viral community metagenomes at both the macro- and micro-diversity levels. Macrodiversity estimates include population abundances and - and {beta}-diversity. Microdiversity calculations include identification of single nucleotide polymorphisms, novel codon-constrained linkage of SNPs, nucleotide diversity ({pi} and {theta}) and selective pressures (pN/pS and Tajima's D) within and fixation indices (FST) between populations. MetaPop will also identify genes with distinct codon usage. Following rigorous validation, we applied MetaPop to the gut viromes of autistic children that underwent fecal microbiota transfers and their neurotypical peers. The macrodiversity results confirmed our prior findings for viral populations (microbial shotgun metagenomes were not available), that diversity did not significantly differ between autistic and neurotypical children. However, by also quantifying microdiversity, MetaPop revealed lower average viral nucleotide diversity ({pi}) in autistic children. Analysis of the percentage of genomes detected under positive selection was also lower among autistic children, suggesting that higher viral {pi} in neurotypical children may be beneficial because it allows populations to better 'bet hedge' in changing environments. Further, comparisons of microdiversity pre- and post-FMT in the autistic children revealed that the delivery FMT method (oral versus rectal) may influence viral activity and engraftment of microdiverse viral populations, with children who received their FMT rectally having higher microdiversity post-FMT. Overall, these results show that analyses at the macro-level alone can miss important biological differences. Conclusions: These findings suggest that standardized population and genetic variation analyses will be invaluable for maximizing biological inference, and MetaPop provides a convenient tools package to explore the dual impact of macro- and micro-diversity across microbial communities.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.11.01.364208v1?rss=1

Authors: Ning, K., Zha, Y., Chong, H., Qiu, H., Kang, K., Dun, Y., Chen, Z., Cui, X.

Abstract:

The taxonomical structure of microbial community sample is highly habitat-specific, making it possible for source tracking niches where samples are originated. Current methods face challenges when the number of samples and niches are magnitudes more than current in use, under which circumstances they are unable to accurately source track samples in a timely manner, rendering them difficult in knowledge discovery from sub-million heterogeneous samples. Here, we introduce a deep learning method based on Ontology-aware Neural Network approach, ONN4MST (https://github.com/HUST-NingKang-Lab/ONN4MST), which takes into consideration the ontology structure of niches and the relationship of samples from these ontologically-organized niches. ONN4MST's superiority in accuracy, speed and robustness have been proven, for example with an accuracy of 0.99 and AUC of 0.97 in a microbial source tracking experiment that 125,823 samples and 114 niches were involved. Moreover, ONN4MST has been utilized on several source tracking applications, showing that it could provide highly-interpretable results from samples with previously less-studied niches, detect microbial contaminants, and identify similar samples from ontologically-remote niches, with high fidelity.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.11.01.363739v1?rss=1

Authors: Lou, J., Zhao, S., Cao, L., Chen, Z., Chan, R. W., Chong, M. K., Zee, B. C., Chan, P. K., Wang, M. H.

Abstract:

Background: During the pandemic of coronavirus disease 2019 (COVID-19), the genetic mutations occurred in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) cumulatively or sporadically. In this study, we employed a computational approach to identify and trace the emerging patterns of the SARS-CoV-2 mutations, and quantify accumulative genetic distance across different periods and proteins. Methods: Full-length human SARS-CoV-2 strains in United Kingdom were collected. We investigated the temporal variation in the evolutionary genetic distance defined by the Hamming distance since the start of COVID-19 pandemic. Findings: Our results showed that the SARS-CoV-2 was in the process of continuous evolution, mainly involved in spike protein (S protein), the RNA-dependent RNA polymerase (RdRp) region of open reading frame 1 (ORF1) and nucleocapsid protein (N protein). By contrast, mutations in other proteins were sporadic and genetic distance to the initial sequenced strain did not show an increasing trend.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.11.02.364273v1?rss=1

Authors: Yazhini, A., Sidhanta, D. S. P., Srinivasan, N.

Abstract:

SARS-CoV-2 spike protein with D614G substitution has become the dominant variant in the ongoing COVID-19 pandemic. Several studies to characterize the new virus expressing G614 variant show that it exhibits increased infectivity compared to the ancestral virus having D614 spike protein. Here, using in-silico mutagenesis and energy calculations, we analyzed inter-residue interaction energies and thermodynamic stability of the dominant (G614) and the ancestral (D614) variants of spike protein trimer in closed and partially open conformations. We find that the local interactions mediated by aspartate at the 614th position are energetically frustrated and create unfavourable environment. Whereas, glycine at the same position confers energetically favourable environment and strengthens intra- as well as inter-protomer association. Such changes in the local interaction energies enhance the thermodynamic stability of the spike protein trimer as free energy difference ({Delta}{Delta}G) upon glycine substitution is -2.6 kcal/mol for closed conformation and -2.0 kcal/mol for open conformation. Our results on the structural and energetic basis of enhanced stability hint that G614 may confer increased availability of functional form of spike protein trimer and consequent in higher infectivity than the D614 variant.

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